intern: Clustering evaluation through internal criteria

In fdm2id: Data Mining and R Programming for Beginners

Clustering evaluation through internal criteria

Description

Evaluation a clustering algorithm according to internal criteria.

Usage

intern(clus, d, eval = "intraclass", type = c("global", "cluster"))

Arguments

clus	The extracted clusters.
d	The dataset.
eval	The evaluation criteria.
type	Indicates whether a "global" or a "cluster"-wise evaluation should be used.

Value

The evaluation of the clustering.

See Also

compare, stability, intern.dunn, intern.interclass, intern.intraclass

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern (km$clus, iris [, -5])
intern (km$clus, iris [, -5], type = "cluster")
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"))
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"), type = "cluster")

fdm2id documentation built on July 9, 2023, 6:05 p.m.